Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 258

The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.

Ternary Blend Organic Solar Cells: Understanding the Morphology from Recent Progress

Xu, Xiaopeng, Li, Ying, Peng, Qiang·2022·Advanced Materials

Abstract

Ternary blend organic solar cells (TB‐OSCs) incorporating multiple donor and/or acceptor materials into the active layer have emerged as a promising strategy to simultaneously improve the overall device parameters for realizing higher performances than binary devices. Whereas introducing multiple materials also results in a more complicated morphology than their binary blend counterparts. Understanding the morphology is crucially important for further improving the device performance of TB‐OSC. This review introduces the solubility and miscibility parameters that affect the morphology of ternary blends. Then, this review summarizes the recent processes of morphology study on ternary blends from the aspects of molecular crystallinity, molecular packing orientation, domain size and purity, directly observation of morphology, vertical phase separation as well as morphological stability. Finally, summary and prospects of TB‐OSCs are concluded.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/adma.202107476

Cites

100 of 100 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 100 references

Cited by

Metadata

Authors
Xu, X., Li, Y., & Peng, Q.
Venue
Advanced Materials
Publisher
Wiley
Published
2022-01-28
Volume
34
Issue
46
Pages
e2107476-e2107476
ISSN
  • 0935-9648 (ISSN-L)
  • 1521-4095
OpenAlex
W3212507570
Cited by
258
References
100
FWCI
12.31
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Ternary Blend Organic Solar Cells: Understanding the Morphology from Recent Progress. https://app.cochat.ai/papers/10.1002/adma.202107476
The paper
Xu, X., Li, Y., & Peng, Q. (2022). Ternary Blend Organic Solar Cells: Understanding the Morphology from Recent Progress. Advanced Materials. https://doi.org/10.1002/adma.202107476

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.